Contents of Lecture. Surface (Terrain) Data Models. Terrain Surface Representation. Sampling in Surface Model DEM

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1 Lecture 13: Advanced Data Models: Terrain mapping and Analysis Contents of Lecture Surface Data Models DEM GRID Model TIN Model Visibility Analysis Geography 373 Spring, 2006 Changjoo Kim 11/29/ Changjoo Kim 11/29/ Surface (Terrain) Data Models Terrain Surface Representation Surfaces represent a continuous field of z-values with an infinite number of points Linking height (z-value) as an attribute to each point (x, y) GIS contain terrain mapping features that allow it to be used in a variety of applications Two data for terrain mapping and analysis DEM: raster, regular grid TIN: Triangulated Irregular Network, vector, irregular sampled points 2-D vs. 3-D Models Volume calculation ESRI software: 2.5-D Microstation: 3-D Changjoo Kim 11/29/ Changjoo Kim 11/29/ Sampling in Surface Model Sampling is necessary to derive an acceptable approximation of a surface in a GIS Z value of new point (unsampled location) is calculated by spatial interpolation from the z value to the closest existing points (sampled locations) DEM Regular array of elevation points A digital file consisting of terrain elevations for ground positions at regularly spaced horizontal intervals Must converted to Raster grid of spot heights for terrain mapping and analysis Simplest and most common representation of topography U.S. Geological Survey DEM Resolution differs depending on the source Attributes of terrain Elevation, slope, aspect (direction of slope) Changjoo Kim 11/29/ Changjoo Kim 11/29/

2 DEM Sources USGS DEM Conversion of contour maps Interpolation Photogrammetry: Satellite image, Aerial photographs DRG: Digital Raster Graphics DLG: Digital Line Graphs DOQ: Digital Orthophoto Quadrangles GPS: Global Positioning Systems LIDAR: Light Detection And Ranging Changjoo Kim 11/29/ Changjoo Kim 11/29/ USGS DLG of Contour Lines (Hypsography) USGS DRG: Scanned, Rectified Topographic Map Changjoo Kim 11/29/ Changjoo Kim 11/29/ USGS DOQ LANDSAT 7 (Satellite image) Changjoo Kim 11/29/ Changjoo Kim 11/29/

3 USGS DEM Five different digital elevation products All are identical in the manner the data are structured Different in the spacing (sampling interval), geographic reference system, areas of coverage, and accuracy The quality of a DEM can influence the accuracy of terrain measures including slope and aspect Changjoo Kim 11/29/ USGS DEM Minute DEM: UTM 1:24,000-scale Grid spacing: 30 by 30 meter Data: 30 meter, 10 meter (resolution) 2. 1-Degree DEM: LAT, LON 1:250,000-scale Grid spacing: 3 by 3 arc-second data spacing Data ~= 90 meter (dependent on the latitude) 3. 2-Arc-Second DEM (30 -minute DEM): LAT, LON 1:,000-scale Grid spacing: 2 by 2 arc-second Minute Alaska DEM: LAT, LON 1:63,360-scale Grid spacing: 2 arc-second of latitude by 3 arc-seconds of longitude Minute Alaska DEM: LAT, LON 1:24,000-scale Grid spacing: 1 arc-second of latitude by 2 arc-seconds of longitude Data: 30 meter, 10 meter Changjoo Kim 11/29/ GRID GRID Slope GRID provides a large suite of modeling functions for performing spatial analysis Distinct modeling characteristics 1. Grid processing is fast 2. Cells are square and readily stack on top of each other for overlay operations 3. The computational complexity of polygon overlay for vector coverages is a very simple operation for grid data because cells from various grid layers stack directly on top of each other 4. y contrast, a polygon overlay operation for vector coverages, in which arcs from one coverage must be intersected with the arcs from another coverage, is computationally complex Slope measures the rate of change of elevation at a surface location Slope determination is based on fitting a surface to the eight neighbors of a central target cell Choose maximum slope on the basis of a comparison of a central target cell with its neighbors Changjoo Kim 11/29/ Changjoo Kim 11/29/ GRID (percent) Slope GRID Aspect Elevation Elevation Slope 21% Slope 40% 141 Horizontal Distance = Digonal Distance = 141 Maximum Vertical Distance = = 30 Slope = 30/141 = Horizontal Distance = Digonal Distance = 141 Maximum Vertical Distance = =40 Slope = 40/= 0.4 The direction of maximum rate of change in z value from each cell (slope) Expressed in positive degrees from 0-360, measured in clockwise from the north Expressed in four or eight principal directions and treat as categorical data Changjoo Kim 11/29/ Changjoo Kim 11/29/

4 GRID Applications GRID Limitation wildlife biologists modeling deer habitat distinct modeling characteristics: habitat factors for deer 1. Distance from water 2. Food 3. Escape cover the flow of water across a terrain surface the spread of fire across a surface of burnability 1. The amount of fuel timber 2. Wind direction 3. Moisture content 4. Aspect of each cell Changjoo Kim 11/29/ Resolution is often an issue for grid data sets GRID can perform many modeling operations using layers of differing resolutions (different cell sizes), the cell size of a grid is a limiting factor for many applications Vector coverages contain the maximum resolution and accuracy available for geographic feature representation For example, feature boundaries of roads, streams, ownership, coastlines, forest stands, and so forth, are clearly delimited in a coverage In a grid representation, such boundaries are typically generalized Once a grid is created, its resolution is established and cannot be improved; it can only be further generalized A new grid containing a smaller cell size must be generated from the original source data to obtain a grid with finer resolution, typically done by converting a coverage to a grid Changjoo Kim 11/29/ GRID Limitation TIN (Triangulated Irregular Network) Operations that rely upon geographic features such as points, lines and polygons are not as efficient using raster data For example, linear feature modeling and network analysis are not possible with grids, and since information rely heavily on feature boundaries, grids are not appropriate for these applications Alternative to regular tessellation Developed in early 1970s as a simple way to build a surface from a set of irregular spaced points TIN does not require a large number of sample points to represent areas where the terrain is relatively uniform TINs are useful for representing surfaces that are highly variable, and contain discontinuities and breaklines Changjoo Kim 11/29/ Changjoo Kim 11/29/ TIN Sources DEMs are primary data source for preliminary TIN Surveyed points LIDAR Contour lines reaklines Figure 14.2 A breakline, shown as a dashed line in (b), subdivides the triangles in (a) into a series of smaller triangles in (c). Changjoo Kim 11/29/ Changjoo Kim 11/29/

5 TIN TIN The main components of a TIN: triangles, nodes and edges Nodes are locations defined by x, y and z values (xyz) from which a tin is constructed s are formed by connecting each node with its neighbors Edges are the sides of triangles Edge Node Changjoo Kim 11/29/ Changjoo Kim 11/29/ TIN: Triangulation Rule Rules The exact structure of a TIN (i.e., which nodes are connected to form triangles) is based upon certain triangulation rules that control tin creation Small equilateral triangles are preferable Changjoo Kim 11/29/ Changjoo Kim 11/29/ How to Create TIN? TIN Data Structure 1. Select sample points 2. Connect points as triangles 3. Select a triangle surface representation 4. Interpolate the whole area Edge List ID A C D E F G H I J K L M Adjacent, E A, C, D, M C, E, K A, D, F E, G F, H G, I, K H, J I, L D, H, L J, K, M C, L Nodes ID A C D E F G H I J K L M Node ID Node List Nodes 1, 3, 4 1, 2, 3 2, 3, 11 3, 5, 11 3, 4, 5 4, 5, 6 5, 6, 7 5, 7, 9 7, 8, 9 8, 9, 10 5, 9, 11 9, 10, 11 2, 10, 11 X, Y, Z x1, y1, z1... x11, y11, z11 4 A E F 6 5 G 7 1 H C 3 D 11 K L 9 J I 8 M 2 10 Changjoo Kim 11/29/ Changjoo Kim 11/29/

6 Interpolation based on TIN TIN Interpolation Using a triangular tessellation of a given point data to drive values at unsampled locations Linear interpolation uses planar facets fitted to each triangle Many possible triangulations for the same vertex set Each triangulation represents different surfaces Estimation of values is different Changjoo Kim 11/29/ Changjoo Kim 11/29/ TIN Interpolation GRID vs. TIN a b c d A oth represent surface GRID raster is simpler than TIN TIN is more accurate than GRID TIN advantages: its ability to create TINs from multiple data sources, and to specify breaklines, surface discontinuities and no-data areas Application TIN properties are especially useful for representing surface elevation and terrain modeling GRIDs are more useful for representing theoretical surfaces such as burnability or environmental cost on top of which spatial process models are run (e.g., modeling the spread of forest fire or identifying the minimum-cost corridor for a new pipeline) Changjoo Kim 11/29/ Changjoo Kim 11/29/ GRID vs. TIN TIN vs. DEM Changjoo Kim 11/29/ Changjoo Kim 11/29/

7 Visibility (Viewshed) Analysis Visibility Analysis Changjoo Kim 11/29/ Changjoo Kim 11/29/ Applications of Terrain Mapping and Analysis Hydrologic modeling Snow cover evaluation Soil mapping Landslide delineation Soil erosion Vegetation communities Changjoo Kim 11/29/